Everyone Can Write a Prompt. Almost Nobody Can Ship an Agent.
Agentic AI across the full lifecycle — reasoning, memory, tools, multi-agent coordination, and production security — built for the skill gap enterprise teams are actually hiring against.
What You Will Architect & Deploy
Gain the end-to-end engineering skills to ship autonomous multi-agent production systems.
Full Lifecycle Agent Architecture
Build and deploy agents across the full lifecycle — reasoning (ReAct, Tree of Thoughts), memory (RAG, vector databases), tools (function calling, CUAs), and multi-agent coordination.
Production Security & Threat Defense
Understand and defend against real agent security risks — prompt injection, tool poisoning, and the STRIDE threat-modeling framework — learning how to build robustly and safely.
Enterprise Protocols (MCP & A2A)
Implement the same interoperability protocols (Model Context Protocol / MCP, Agent-to-Agent / A2A) that frontier labs and enterprise engineering teams are standardizing on.
Is This Course Right For You?
Designed for developers and technical leaders building serious autonomous systems.
This is for you if:
- You've used ChatGPT or Claude APIs but have never built an autonomous, tool-executing agent.
- Your engineering team is evaluating agentic AI for a real enterprise workflow, not just a proof-of-concept toy.
- You want production-grade security, observability, and evaluation content, not just another basic prompting tutorial.
This is NOT for you if:
- You want a no-code drag-and-drop agent builder without understanding the underlying Python or protocol layers.
- You're looking for a quick five-minute prompting trick rather than a full engineering lifecycle.
Direct On-Demand Masterclass Access
Get immediate, lifetime access to all 38+ hours of video lectures, hands-on build labs, code repositories, and verifiable certificate of completion on Udemy.
Enroll Directly on Udemy
Learn at your own pace with lifetime access to all 38+ hours of video lessons, code repositories, MCP server setups, and protocol implementations.
- Autonomous reasoning loops: ReAct, Plan-and-Solve, Tree of Thoughts
- Advanced memory: Hybrid vector search, reranking & 6-stage RAG
- Multi-agent orchestration, MCP & A2A protocol implementations
- Lifetime access + downloadable production code repos & certificate
Comprehensive 7-Module Curriculum
From core reasoning loops to multi-agent protocols and production guardrails.
- The 4 core building blocks: Perception, Reasoning, Memory, and Planning
- LLM reasoning limitations: Hallucination, tool misuse, and grounding failures
- Threat modeling and safety fundamentals (Prompt injection & STRIDE framework)
- The 2025 agentic ecosystem: Model Context Protocol (MCP) and Agent-to-Agent (A2A) standards
- Engineering vs guesswork: Chain-of-Thought, Self-Consistency, and Tree of Thoughts (ToT)
- The full ReAct framework (Reason + Act) and its self-correcting variants (Reflexion)
- Plan-and-Solve patterns: Dynamic replanning and backtracking
- Treating prompts as versioned code: Eval suites, regression gates, and A/B benchmarking
- The 3 memory layers: Working memory (scratchpad), short-term (context), long-term (episodic/semantic)
- The complete 6-stage RAG pipeline: Ingestion, chunking, embedding, hybrid retrieval, reranking, synthesis
- Vector databases (Qdrant, Pinecone, Chroma) with BM25 + dense hybrid search
- Cross-encoder rerankers and contextual compression for noise reduction
- Schema design and JSON-schema strict validation for tool calling
- Function/tool calling mechanics: Parallel tool calling and error escalation
- Tool chaining and sandboxed Python code execution environments
- Advanced archetypes: Coding assistants and Computer Using Agents (CUAs / browser use)
- Manager/worker hierarchies vs peer debate-based architectures
- Inter-agent communication: Direct messaging, shared blackboard, and pub-sub queues
- Human-in-the-loop (HITL) integration: Approval gates, pause-and-resume workflows
- The deep-research agent pattern: Parallel sub-agent dispatch and synthesis
- Building automated eval suites with synthetic test data and LLM-as-a-judge
- Tracing and distributed observability with LangSmith and OpenTelemetry
- Quantifying reasoning degradation, tool misuse rate, and trajectory deviation
- Automated regression testing gates in CI/CD pipelines
- Scalable orchestrator/supervisor architectures on Kubernetes and serverless backends
- Guardrails and input/output filters (NeMo Guardrails, Llama Guard)
- Cost, token caching, and latency optimization in high-throughput environments
- Security risks specific to interoperability protocols: Endpoint trust and tool poisoning
Agentic AI FAQs
Does this course rely on black-box frameworks like AutoGen or CrewAI?
No. While we explore popular framework patterns, we build reasoning loops, memory systems, tool parsers, and multi-agent coordination from first principles in raw Python. This ensures you understand the underlying mechanics and can debug failure modes in any stack.
Do we cover the new Model Context Protocol (MCP)?
Yes! Module 1 and Module 7 cover MCP server and client architectures, tool exposure protocols, and Agent-to-Agent (A2A) communication patterns, including security risks like tool poisoning.
What background do I need before taking this course?
You should be comfortable with intermediate Python and basic REST APIs / LLM prompting. If you need a refresher on OOP and clean code, start with Mastering Python.
Vinit Singh
AI Consultant & Principal AI Architect, gadgap AI
Vinit is an AI Consultant and Educator specializing in LLM Fine-Tuning, Voice AI, Speech Language Models, Agentic AI, and Computer Vision — with over 18 years of experience in Data Science and Artificial Intelligence. A graduate of IIT Bombay with Stanford Machine Learning and Deep Learning certifications, he works at the intersection of AI research and real-world deployment.
Currently a Consultant in the Speech & Language team at Sony India Software Centre, his prior work spans Computer Vision on Nvidia edge hardware at Assert AI, and end-to-end AI consulting at tvam Technologies — where he built agentic FinTech workflows, a robo-advisor via LoRA fine-tuning on DeepSeek-R1, and a Voice AI telecaller pipeline.
A top 3% Udemy creator globally, trusted by learners across 150+ countries and enterprises including Adidas, Barclays, and Volkswagen — covering Voice AI, Agentic AI, Computer Vision, and NLP & LLMs.
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